Abstract: Systems, methods, and a computer readable medium are provided for generating recommendations based on semantic knowledge capture. Event data generated by domain-specific entities can be received, formatted, and aggregated so that ontological mappings can be applied to generate domain-specific event data. The ontological mappings identify relationships associated with the event data generated by two or more communicatively coupled domain-specific entities. A graph can be generated based on the domain-specific event data and provided to a user via a GUI. The graph can include nodes representing domain-specific entities and a edges representing one or more contextual relationships between two or more nodes sharing event data. The user can provide query input via the GUI to generate an updated graph corresponding to the query input. Recommendations can be provided to the user based on the query input and the contents of the updated graph.

Abstract: A simulation computer device for securely executing a model includes at least one processor in communication with at least one memory device. The simulation computer device is configured to store a smart container including a model and a usage policy. The simulation computer device is also configured to receive a plurality of inputs for the model and determine whether to validate the model based on the usage policy. The simulation computer device is further configured to execute the model with the plurality of inputs if the model was validated. Moreover, the simulation computer device is configured to transmit at least one output.

Abstract: A simulation computer device for securely executing a model includes at least one processor in communication with at least one memory device. The simulation computer device is configured to store a smart container including a model and a usage policy. The simulation computer device is also configured to receive a plurality of inputs for the model and determine whether to validate the model based on the usage policy. The simulation computer device is further configured to execute the model with the plurality of inputs if the model was validated. Moreover, the simulation computer device is configured to transmit at least one output.

Abstract: A system for optimizing processing and display of large datasets is provided. The system includes a graphics processing and optimization (GPO) computing device. The GPO computing device is configured to store a dataset including a data point in a memory device, select the data point to display on a display device based on a first display request signal received via a user interface, and accelerate graphical processing of the dataset using optimization algorithms, wherein the GPO computing device is configured to assign a worker process to execute on the data point, The GPO computing device is also configured to convert the data point into a renderable graphics component, and generate a least-compute intensive representation of the data point, and cause the user interface to display a graphical representation of a first subset of the data including the renderable graphics component at a first display resolution.

Abstract: A system for optimizing processing and display of large datasets is provided. The system includes a graphics processing and optimization (GPO) computing device. The GPO computing device is configured to store a dataset including a data point in a memory device, select the data point to display on a display device based on a first display request signal received via a user interface, and accelerate graphical processing of the dataset using optimization algorithms, wherein the GPO computing device is configured to assign a worker process to execute on the data point, The GPO computing device is also configured to convert the data point into a renderable graphics component, and generate a least-compute intensive representation of the data point, and cause the user interface to display a graphical representation of a first subset of the data including the renderable graphics component at a first display resolution.

Abstract: Systems, methods and computer program products to automate the process of ontology updates in radiology software are provided. In one aspect, the present disclosure analyzes the textual data describing the radiology exams and identifies terms that are not defined in the existing ontology. It then extracts various types of statistical patterns, such as neighboring concepts, from the textual data, and infers which concepts the unrecognized terms belong to. Finally it presents rank-ordered ontology updating suggestions to the user for final confirmation. The system, methods, and computer program products of the present disclosure are an effective way in updating ontologies, requiring the users to have little, or no prior experience in ontology management or understanding of the underlying ontology structure.

Abstract: Certain embodiments of the present invention provide methods and systems for determining a hanging protocol for display of clinical images in a study. Certain embodiments provide a machine learning hanging protocol analysis system. The example system includes an image processing module to process image data to provide one or more features. The example system includes a learning engine to receive processed image data and additional data to learn and adapt a hanging protocol for repeated use by applying one or more machine learning algorithms to the processed image data and additional data. The learning engine is to continue to refine an available selection of candidate layouts based on the processed image data and additional data to provide one or more layout choices for selection to form a hanging protocol for display of image and other data.

Abstract: Certain embodiments of the present invention provide methods and systems for determining a hanging protocol for display of clinical images in a study. Certain embodiments provide a machine learning hanging protocol analysis system. The example system includes an image processing module to process image data to provide one or more features. The example system includes a learning engine to receive processed image data and additional data to learn and adapt a hanging protocol for repeated use by applying one or more machine learning algorithms to the processed image data and additional data. The learning engine is to continue to refine an available selection of candidate layouts based on the processed image data and additional data to provide one or more layout choices for selection to form a hanging protocol for display of image and other data.